1. Field of the Inventions
The field of the invention relates generally to automated processing of knowledge and more particularly to application of an abstract model of information that provides certain advantages in access, visualization, discovery and processing.
2. Background Information
Automation of information processing has been a goal of inventions and machine design since humans conceived and built the earliest apparatus. Seeking advanced processing schemes continues to be a field of great interest and with great potential to advance scientific understanding and be employed to generate wealth and benefit humanity.
With the advent of computing machinery, advances in available technologies such as computing power, ready availability of processing, disk storage and data networks have created an interesting set of circumstances. These new technologies make possible the implementation of many models of information processing that previously could only be conceptual or thought experiments. However, the same technology has also enabled the creation and storage of massive amounts of stored data and information, in turn creating a need for advanced and automated techniques to process the large quantity stored data.
The modeling of information and cognition is useful in many of the sciences, including computer understanding, cognitive sciences, neurobiology, macro-conscious organizations, education, decision support systems and fundamental physics. Modeling and automation of information processes according to specialized models has been proposed as a key to understanding consciousness and endowing computers with higher order cognitive functions and understanding, perhaps allowing computers to make discoveries and attain capabilities matching or exceeding human limits.
The term Artificial Intelligence (AI) broadly covers research and implementation directed at allowing machines to exhibit human-like cognition abilities including thought, comprehension, reasoning, learning, problem solving, abstraction, observation and explanation. Any attempt to make machines behave in a human-like way or show intelligent behavior is often described as artificial intelligence.
Many conventional approaches to artificial intelligence separate the AI implementation into two areas: a knowledge base and a knowledge engine. The knowledge base captures knowledge about a particular domain in a machine-usable format. The knowledge engine applies the knowledge base. In other terms the knowledge base is analogous to logic or rules while the knowledge engine is functional instructions, algorithms and processing instructions that apply the logic. However, many AI implementations blur the distinction between logic and application. For example, logic may include processing instructions.
Designing and building a knowledge base is a primary activity of knowledge engineering. The term knowledge engineering was first used by Edward Feigenbaum in 1983. Feigenbaum defined knowledge engineering as an engineering discipline that involves integrating knowledge into computer systems in order to solve complex problems normally requiring a high level of human expertise. Often domain experts provide knowledge for a knowledge base, and the expert knowledge may be converted into the knowledge base automatically or by human action. A particular challenge is encoding the knowledge in a format that makes it readily accessible both in an AI application and to maintainers and creators.
One conventional approach to designing a knowledge base captures the knowledge in a sequence of rules, each rule comprising an antecedent part and a consequent part. The antecedent is a series of conditions that, when satisfied, trigger the actions specified in the consequent part. The rules are then processed against a set of input data to yield one or more final conclusions. Although rule-based expert systems have achieved success in limited domains, a challenge is capturing the domain knowledge in a series of rules. Most conventional systems have required a domain expert to work with a knowledge engineer to capture the rules. Another challenge facing rule-based systems is applying the rules in an expedient manner, limiting use of rule-based systems in time-critical systems. It is often debated whether human experts use rules in reaching conclusions and correspondingly the value of automated application of rules.
A conventional approach to building a model of domain knowledge is training, wherein an automated observer examines a series of events and conditions, consequently constructing a model of the domain knowledge based on the observations. In supervised learning (also called reinforcement training) a teacher or other source of expertise guides the training by providing correct outcomes. In unsupervised learning the automated learning system looks for patterns or features in a data set without guidance. Some conventional systems have attempted to automatically build rules from observed data.
Another approach to artificial intelligence employs an artificial neural network. Networks of biological neurons comprise much of the brain of humans and other creatures. Artificial neural networks use analogous principles, using computing algorithms or hardware to implement artificial nodes or neurons that interconnect. Each node may receive stimulation from its input network and when adequately stimulated, the node “fires” and delivers stimulation to nodes connected to its output. The connections and firing thresholds determine the function of the network. Neural networks may be generalized to the field of connectionism.
Although early work in neural networks focused on emulating biological system structures and functions, much modern work is based on signal processing and statistics. A problem with neural networks is the requirement that they be trained on a knowledge domain. Training may be time-consuming and the knowledge gained is specific to the training data. Thus artificial neural networks do not generalize or gain the ability to address data outside the training domain. Neural networks have achieved success in limited domains such as control systems.
Classifiers are automated systems for dividing a set of data into classes, recognizing patterns in data, and in general automatically identifying relations in a dataset. Such relations may include correlations, rankings, clusters and features. Many classifiers are implemented using kernel methods or support vector machines. Kernel methods have achieved some success in fields such as handwriting analysis and automated character recognition.
Verification and validation is another challenge of computer-based intelligent systems. Many mission-critical systems require intensive testing to verify that a system meets a specification and also validate that the system performs as expected before the system may be deployed. This testing is difficult when the knowledge is captured in a format not easily understood by humans and the system behavior is complex or non-linear, as is often the case with automated cognitive implementations.
Other related fields are cognitive modeling, information theory, and Kolmogorov complexity.